This study examined the feasibility and utility of collecting data on characteristics of patients attending outreach camps as a way of assessing equity of access to eye care services. Data were collected from a random sample of people presenting at the outreach camps in one of the districts in Central Malawi. We were specifically interested in patients’ socio-economic and disability status and used existing standardised international tools to collect these variables. For socio-economic status we measured both absolute poverty rates and relative wealth.
We found that using a set of selected tools to routinely monitor equity in service delivery is feasible; and although data collection did require additional human resources, their work was neither intrusive nor disruptive to the work of the medical personnel. Indeed, CATCH staff who worked alongside the data collection team confirmed that surveying study participants had minimal effect on their usual camp routine. Collecting data from a sample of patients at more convenient time rather than all camp attendees required longer presence of data collectors at the camp but it prevented interference with the flow of patients. Collecting additional data after the initial screening was the most optimum point, as by that time the patients had received the service they came for they were either on their way home (if they did not have any eye problems) or were waiting at the camp for further examination and treatment.
The study also helped to better understand the use of the tools for measuring patients’ socio-economic status in a routine service delivery setting. The EquityTool and the Poverty Scorecard used in this study rely on asset-based wealth indexes which are considered as best practices for household surveys, where dwelling characteristics and ownership of durable assets can be directly observed. There were concerns about the reliability of self-reported data, but this study found a high level of agreement (80%) between the information given by respondents at the point of service delivery and what was observed during the follow-up visits to their homes. This suggests that self-reported data on dwelling characteristics and assets can be a reliable measure of socio-economic wealth and that the tools, such as the EquityTool and the Poverty Scorecard, can be applied to collect data from participants presenting at clinics or outreach camps. Both questionnaires are simple and fast to administer (10 to 15 minutes) causing minimal disruption to health care services.
In terms of absolute wealth measurement, the Poverty Scorecard results showed that camp participants’ average poverty rate was significantly higher than the poverty rates of Kasungu and national population of Malawi in 2011 (24). The camp participants’ poverty rate was also higher than the national poverty rate measured in 2017 (26). However, the poverty rates were similar to the levels in Kasungu. The findings suggest that the programme reached poor people in a proportion similar to the Kasungu population. The relative wealth measurement and the EquityTool results indicate that camp attendees were relatively wealthier compared to the 2010 population of Kasungu and at the national level (25). However, compared to the DHS 2017, the camp attendees appeared to be relatively poorer than the Kasungu and national populations, which is typical for trachoma endemic areas as trachoma is known to be a disease of poverty affecting the poorest and most marginalised communities (36) .
We found the use of both tools necessary and complementary, as the Poverty Scorecard shows how poor your target population is in absolute terms, while the EquityTool assesses programme populations in comparison with the national population or the urban population of a country. However, it is important to note that the comparison of our camp attendees with the population of Kasungu rather than the national population produced more relevant results to measure equitable access of a local health programme. The finding suggests the importance of using regional and district level subsets of national survey data as benchmarks for comparison.
It is also important to note that the latest versions of the tools available to us at the time of the study (in 2017) were based on relatively old surveys (DHS 2010 and IHS 2010/2011) [6, 9]; the tools have since been updated based on more recent surveys (DHS 2015-16 and IHS 2017). This is likely to explain the fact that our participants were generally wealthier when compared to the national and Kasungu district populations in 2010-11 (as compared to more recent surveys). The results confirm an earlier observation made by Wilunda et al. that tools based on the ownership of assets tend to lose their reliability over time (30). It is particularly true for the ownership of assets such as radios, televisions or mobile phones, which can rapidly change over a short period of time. Therefore, to determine the economic status more accurately it is essential to use the most recent household survey benchmarks and tools (37). This finding is important to consider when integrating equity measurement in a development project cycle. For example, in the contexts where only a relatively old (five years or more) tool for measuring wealth is available, it may be better to wait for the release of an updated tool, as the conclusions based on the data from the old tool are likely to be inaccurate and misleading.
The estimated prevalence of functional limitations among programme participants attending the outreach camps was 27.5%, including all domains, and 14.2%, excluding the sight domain when using the Washington Group definition of disability. Disability data collected at the facility level is difficult to interpret, as people coming to the facilities are not necessarily representative of the general population. In addition, people coming to eye care services, are usually older and have visual impairments but population-based data on the prevalence of additional disabilities in people with visual impairments is limited, as very few RAABs integrate disability measurement in their design. However, we could compare our data with a recent survey of living conditions of people with disabilities in Malawi published in 2018 (38). The survey used the same WGSS tool and collected disability data in a large sample of over 30,000 people aged 2 + years. Prevalence of disability among adults was 9%. Prevalence of disability among people 50 + years was 20.2%, which shows that our results are broadly comparable to the population-based data in Malawi suggesting that the camps were accessible for people with disabilities.
Intersectionality between disability and poverty has been of great interest to academic literature (39–42). Indeed poverty can increase the risk of disability and, conversely, disability can trap people in poverty because of the barriers disabled people face in taking part in education, employment, social activities and other aspects of life (43–45), Therefore, our study had the objective of examining and adding empirical evidence of the association between disability and poverty. Results showed that the odds of belonging to the wealthiest quintiles while reporting a disability (visual or non-visual) were statistically lower (respectively p = 0.000 and p = 0.019). However, there were no relationship between disability and poverty in absolute terms. This is likely to be because a very large proportion of our participates were below the poverty line and a large proportion had a disability. Even though the absolute measure of wealth did not show statistical links, the relative wealth analysis indicates differences and suggests that programmes should systematically include inclusive services and disaggregate monitoring data to ensure no one is left behind.
Another aspect of equity that may require further attention is gender equity. Outreach camps in the CATCH programme targeted primarily people with cataract and trachomatis trichiasis. For both conditions, women are at higher risk than men. Yet, 54% of our study participants were male. Gender inequities in accessing eye health services have been well documented (9, 46–50). In most low-income settings in Sub-Saharan Africa, women have lower coverage with cataract services and are more likely to be blind and severely visually impaired than men. The reasons for this are multiple and complex, ranging from cultural norms that value males over females to women’s inability to pay or travel outside their community (49–51). Our study suggests that women are not only less likely to accept and access treatment, but that they are less likely to attend the first point of contact with a healthcare provider, which is outreach camps. Further studies are needed to explore the drivers of women’s health seeking behaviour and to better understand whether women do not have access to information about outreach camps or they do not come due to household, childcare or other duties.
There are several challenges and limitations that need to be considered when interpreting these results or when planning similar studies in other locations. First, our conclusions are limited to one location, Kasungu, and cannot be generalised to other settings doing similar programmes in Malawi or elsewhere. Studies in other parts of Malawi and other countries would be beneficial to review equity in accessing eye health services in a more systematic way and whether there are any cross-country generalisations. Second, in this study we did not have access to clinical data. The data collected here was only on the attendance of outreach camps. We do not know whether there were differences in the severity of visual impairment presented at the camp or the uptake of referrals by gender, socio-economic status, or disability.
Finally, although the residence of programme participants (village and traditional authority area) was recorded in this study, it could not be classified into urban or rural as per the DHS. We did not map patients’ residence in relation to the location of the camps and did not assess whether there were any specific locations that could not be reached due to distance. Future outreach camps need to collect more accurate data on attendees’ residence and distance travelled, possibly using GIS maps. This will help to better understand the intersectionality of wealth, rural residence, gender, and disability.